Dynamic customization of an autonomous vehicle experience

ABSTRACT

Dynamic customization of an autonomous vehicle experience is presented herein. A dynamic recommendation engine can comprise a subscriber interface component, a data component, and a configuration component. The subscriber interface component can receive, from a subscriber of an autonomous vehicle service, a request specifying a route of transport by an autonomous vehicle; and based on the request, the data component can obtain, via a network slice comprising a virtual network function of the autonomous vehicle service, profile information for the subscriber and route information for the route. Further, the configuration component can determine, via the network slice based on the profile information and the route information, configuration data for the autonomous vehicle, and send, via the network slice, the configuration data directed to the autonomous vehicle to facilitate the transport by the autonomous vehicle.

TECHNICAL FIELD

The subject disclosure generally relates to embodiments for dynamiccustomization of an autonomous vehicle experience.

BACKGROUND

Conventional autonomous vehicle technologies can facilitate driving apassenger to a location, without an autonomous vehicle being controlledby the passenger. Consequently, such technologies have had somedrawbacks with respect to tailoring a driving experience of thepassenger according to passenger preferences, some of which may be notedwith reference to the various embodiments described herein below.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting embodiments of the subject disclosure are described withreference to the following figures, wherein like reference numeralsrefer to like parts throughout the various views unless otherwisespecified:

FIG. 1 illustrates a block diagram of an autonomous vehicle ecosystemcomprising an autonomous vehicle service slice and a dynamicrecommendation engine, in accordance with various example embodiments;

FIG. 2 illustrates a block diagram of a dynamic recommendation engine,in accordance with various example embodiments;

FIG. 3 illustrates a block diagram of an autonomous vehicle system, inaccordance with various example embodiments;

FIGS. 4-8 illustrate flowcharts of methods associated with a dynamicrecommendation engine, in accordance with various example embodiments;

FIG. 9 illustrates a block diagram of a wireless network environment, inaccordance various example embodiments; and

FIG. 10 is a block diagram representing an illustrative non-limitingcomputing system or operating environment in which one or more aspectsof various embodiments described herein can be implemented.

DETAILED DESCRIPTION

Aspects of the subject disclosure will now be described more fullyhereinafter with reference to the accompanying drawings in which exampleembodiments are shown. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea thorough understanding of the various embodiments. However, thesubject disclosure may be embodied in many different forms and shouldnot be construed as limited to the example embodiments set forth herein.

Conventional vehicle technologies have had some drawbacks with respectto custom tailoring of a driving experience of a passenger of anautonomous vehicle according to preference(s) of the passenger. Variousembodiments disclosed herein can improve passenger experience(s) byconfiguring, via a network slice associated with a virtual networkfunction, an interior environment of an autonomous vehicle based on aprofile of a passenger, a class of service that the passenger hassubscribed to, and properties of a route that has been requested by thepassenger.

For example, in an embodiment, a system, e.g., a dynamic recommendationengine, can comprise a subscriber interface component, a data component,and a configuration component. The subscriber interface component canreceive, from a subscriber of an autonomous vehicle service, a requestspecifying a route of transport by an autonomous vehicle, e.g.,operating without direct control by the subscriber; and based on therequest, the data component can obtain, via a network slice comprising avirtual network function of the autonomous vehicle service, profileinformation for the subscriber and route information for the route.Further, the configuration component can determine, via the networkslice based on the profile information and the route information,configuration data for the autonomous vehicle, and send, via the networkslice, the configuration data directed to the autonomous vehicle tofacilitate the transport by the autonomous vehicle.

In one embodiment, the network slice corresponds to a group of networkslices comprising respective virtual software network functions ofrespective vehicle services comprising the autonomous vehicle service.In this regard, network slices of the group of network slices correspondto different control and user planes, e.g., to facilitate control ofdifferent autonomous vehicles according to the respective vehicleservices. In another embodiment, in response to the request beingreceived, the network slice can be instantiated in a memory of thesystem to provision the autonomous vehicle service via the autonomousvehicle.

In embodiment(s), the profile information can represent a selection of,e.g., a type of the autonomous vehicle (e.g., size, brand, model, etc.of a preferred autonomous vehicle); a class, preferred class, etc. ofthe autonomous service (e.g., luxury, sport, tourist, etc.); an interiorconfiguration of the autonomous vehicle (e.g., a temperature of aninterior of the autonomous vehicle, a seating configuration of theinterior, an entertainment configuration (e.g., with respect to apreferred music genre, movie genre, sport/sports team, news service,etc. corresponding to sound, radio, video, etc. device(s) of theinterior), etc.

In other embodiment(s), the profile information represents a preferenceof the subscriber corresponding to another route the subscriber hastraveled on. In this regard, the other route can satisfy a definedcondition with respect to being similar to the route with respect to,e.g., a distance of the route, a geographical location of the route, apurpose (e.g., as a commuter, a tourist, a business traveler, etc.) forthe request, etc.

In yet other embodiment(s), the profile information represents apreference of a passenger/driver of a second vehicle, e.g., autonomousvehicle, non-autonomous vehicle, etc. that has traveled along the route,along another route that has been determined to be similar to the routewith respect to, e.g., a geographic location of the route, a distance ofthe route, etc.

In an embodiment, the route information can represent trafficinformation for the route that has been obtained by the data component,e.g., from traffic information service(s), sensor device(s) that havebeen installed along the route, etc. In another embodiment, the routeinformation can represent police and/or emergency responder, e.g., fire,tow truck, etc. activity along the route; construction activity alongthe route, weather condition(s) (e.g., precipitation, wind, etc.)corresponding to the route, etc.

In embodiment(s), the configuration data can specify a configuration of,e.g., a temperature of an interior of the autonomous vehicle, a seatingconfiguration of the interior, a multimedia configuration of theinterior, a direction of the autonomous vehicle, an acceleration of theautonomous vehicle, etc. In this regard, in an embodiment, based on theroute information representing, e.g., high traffic occurring on theroute, police and/or emergency responder activity occurring along theroute, construction activity occurring along the route, etc., theconfiguration component can generate the configuration data tofacilitate control of navigation device(s), acceleration device(s),braking device(s), steering device(s), etc. of the autonomous vehiclefor transport of the subscriber to a destination of the route along adetour of the route, e.g., the detour determined, by the data component,to be associated with less traffic, police and/or emergency responder,construction, etc. activity than such activities corresponding to theroute.

In one embodiment, a method can comprise instantiating, by a systemcomprising a processor, a network slice of an autonomous vehicle serviceas a virtual network function to facilitate a transport, via anautonomous vehicle, of a subscriber, e.g., identified by a subscriberidentity of the autonomous vehicle service, according to a route thathas been specified by a request associated with the subscriberidentity—in response to receiving, by the system, the request associatedwith the subscriber identity. Further, in response to a profile of thesubscriber identity and route information of the route being obtained bythe system using the network slice, the system can determine, based onthe profile and the route information using the network slice,configuration data, and send, using the network slice, the configurationdata directed to the autonomous vehicle to facilitate the transport ofthe subscriber.

In embodiment(s), the instantiating comprises instantiating the networkslice using a control plane—the control plane being different fromanother control plane corresponding to an instantiation, as anothervirtual network function, of another network slice, e.g., correspondingto another autonomous vehicle service.

In other embodiment(s), obtaining the profile of the subscriber identitycomprises obtaining information presenting an environmental preferencefor an interior of the vehicle, a seating configuration for theinterior, an entertainment configuration for the interior, a preferenceof a passenger/driver of another vehicle corresponding to the route,etc.

In an embodiment, the determining of the route information can comprisedetermining a traffic condition of the route. In turn, the determiningof the configuration data can comprise determining, based on the trafficcondition, a detour for the route.

In another embodiment, a machine-readable storage medium can compriseexecutable instructions that, when executed by a processor, facilitateperformance of operations, comprising: receiving, from a subscriber of avehicle service, a request for transportation, via an autonomousvehicle, to a destination; based on the request, obtaining, via anetwork slice comprising a virtual network function, a subscriberprofile of the subscriber and route information of a route correspondingto the destination; and based on the subscriber profile and the routeinformation, generating, via the network slice, configuration data forthe autonomous vehicle, and sending, via the network slice, theconfiguration data directed to the autonomous vehicle to facilitate thetransportation to the destination.

In one embodiment, the virtual network function corresponds to a firstcontrol plane that is different than a second control planecorresponding to another virtual network function, e.g., correspondingto another vehicle service. In another embodiment, the generating theconfiguration data comprises generating detour information to facilitatethe transportation to the destination via a detour.

Reference throughout this specification to “one embodiment,” “anembodiment,” etc. means that a particular feature, structure, orcharacteristic described in connection with the embodiment is includedin at least one embodiment. Thus, the appearances of the phrase “in oneembodiment,” “in an embodiment,” etc. in various places throughout thisspecification are not necessarily all referring to the same embodiment.Furthermore, the particular features, structures, or characteristics maybe combined in any suitable manner in one or more embodiments.

As mentioned above, conventional vehicle technologies have had somedrawbacks with respect to tailoring a transportation experience of apassenger of an autonomous vehicle according to preference(s) of thepassenger. To address these and other concerns of such technologies,various embodiments disclosed herein can improve passenger experience(s)by configuring, via a network slice associated with a virtual networkfunction, an autonomous vehicle based on: a profile of a passenger ofthe autonomous vehicle, a class of service that the passenger hassubscribed to, and properties of a route that has been requested by thepassenger.

In this regard, and now referring to FIGS. 1 and 2, block diagrams of anautonomous vehicle ecosystem (100)—comprising an autonomous vehicleservice slice (120) of an autonomous vehicle service—and a dynamicrecommendation engine (110) are illustrated, respectively, in accordancewith various example embodiments. Autonomous vehicle ecosystem 100 cancomprise an autonomous vehicle (e.g., 130, 150) that is wirelesslyconnected, via a wireless network 101, to dynamic recommendation engine110 of autonomous vehicle service slice 120—autonomous vehicle serviceslice 120 comprising a virtual network function of the autonomousvehicle service.

Dynamic recommendation engine 110 can comprise data component 210,configuration component 220, and subscriber interface component 230. Inthis regard, subscriber interface component 230 can receive, from asubscriber of the autonomous vehicle service, a request specifying aroute (141) of transport by the autonomous vehicle. Further, based onthe request, data component 210 can obtain, via autonomous vehicleservice slice 120, profile information for the subscriber and routeinformation for the route. In an embodiment, data component 210 canstore/retrieve the profile information from data store 122.

In turn, configuration component 220 can determine, via autonomousvehicle service slice 120 based on the profile information and the routeinformation, configuration data for the autonomous vehicle, and send,via autonomous vehicle service slice 120, the configuration datadirected to the autonomous vehicle to facilitate the transport by theautonomous vehicle.

In an embodiment, autonomous vehicle service slice 120 can correspond toa group of network slices (not shown) comprising respective virtualsoftware network functions of respective vehicle services comprising theautonomous vehicle service. In this regard, network slices of the groupof network slices can correspond to different control and user planes,e.g., to facilitate control of different autonomous vehicles accordingto the respective vehicle services. In one embodiment, in response tothe request being received, autonomous vehicle service slice 120 can beinstantiated in a memory of autonomous vehicle ecosystem 100 toprovision the autonomous vehicle service via the autonomous vehicle.

In other embodiment(s), as illustrated by FIG. 1, data component 210 canwirelessly communicate, via wireless network 101, with the autonomousvehicle, other vehicles (e.g. a non-autonomous vehicle, an autonomousvehicle), sensors installed along the route (e.g., 142, 144), etc. via awireless interface (e.g., 114, 116) utilizing an access point (AP) (e.g.104, 106, 146), e.g., a macro AP, a Femto AP, a pico AP, a base station,etc. The wireless interface can comprise an over-the-air wireless linkcomprising a downlink (DL) and an uplink (UL) (both not shown) that canutilize a predetermined band of radio frequency (RF) spectrum associatedwith, e.g., cellular, LTE, LTE advanced (LTE-A), GSM, 3GPP universalmobile telecommunication system (UMTS), Institute of Electrical andElectronics Engineers (IEEE) 802.XX technology (WiFi, Bluetooth, etc.),worldwide interoperability for microwave access (WiMax), a wirelesslocal area network (WLAN), Femto, near field communication (NFC),Wibree, Zigbee, satellite, WiFi Direct, etc. Accordingly, wirelessnetwork 101 can be associated with RF spectrums corresponding torespective types of wireless technologies including, but not limited to,cellular, WiFi, WiMax, WLAN, Femto, NFC, Wibree, Zigbee, satellite, WiFiDirect, etc.

In yet other embodiment(s), dynamic recommendation engine 110 can becommunicatively coupled, via autonomous vehicle service slice 120, towireless network 101 utilizing one or more of the Internet (or anothercommunication network (e.g., an Internet protocol (IP) based network)),a digital subscriber line (DSL)-type or broadband network facilitated byEthernet or other technology, and/or wireless interface(s), e.g.,cellular, WiFi, WiMax, WLAN, Femto, NFC, Wibree, Zigbee, satellite, WiFiDirect, etc. In this regard, components, portions, etc. of autonomousvehicle ecosystem 100 can comprise a cloud-based, centralized,communication platform, Internet platform, WAN, etc. In turn,component(s), portion(s), etc. of autonomous vehicle service slice 120,e.g., dynamic recommendation engine 110, can be implemented within thecloud-based, centralized, communication platform.

In embodiment(s), the profile information for the subscriber canrepresent a selection of, e.g., a type of the autonomous vehicle (e.g.,size, brand, model, etc. of a preferred autonomous vehicle); a class,preferred class, etc. of the autonomous vehicle service (e.g., luxury,sport, tourist, etc.); an interior configuration of the autonomousvehicle (e.g., a temperature of an interior of the autonomous vehicle, aseating configuration of the interior, an entertainment configuration(e.g., with respect to a preferred music genre, movie genre,sport/sports team, news service, etc. corresponding to sound, radio,video, etc. device(s) of the interior), etc.

In this regard, in one embodiment, data component 210 can be configuredto determine the profile information based on subscriber informationreceived, via subscriber interface component 230, from the subscriber.In another embodiment, data component 210 can be configured to determinethe profile information based on a wireless profile of the subscriber,e.g., representing multimedia content, e.g., music downloads, musicstreaming, video content, news, videos, etc. that the subscriber hasaccessed, e.g., via a provider of a wireless communication service, fromthe Internet utilizing a wireless device, cellular phone, smart watch,etc.

In other embodiment(s), the profile information can represent apreference of the subscriber corresponding to another route (not shown)that the subscriber has traveled on. In this regard, the other route canbe selected, e.g. by data component 210, in response to a determinationthat the other route satisfies a defined condition with respect to beingsimilar to the route, e.g., with respect to a distance of the route, ageographical location of the route, a purpose of transportation, e.g.,obtained by subscriber interface component 230, of the subscriber (e.g.,representing whether the subscriber is a commuter, a tourist, a businesstraveler, etc. for the route), etc.

In yet other embodiment(s), the profile information can represent apreference of a passenger/driver of a second vehicle, e.g., anotherautonomous vehicle, a non-autonomous vehicle, etc. that has traveledalong the route, along another route that has been determined to besimilar to the route with respect to, e.g., a geographic location of theroute, a distance of the route, elevation of the route, etc.

In an embodiment, the route information can represent trafficinformation for the route that has been obtained by the data component,e.g., from traffic information service(s), sensor device(s), e.g.,camera(s) that have been installed along the route, etc. In anotherembodiment, the route information can represent police and/or emergencyresponder, e.g., fire, tow truck, etc. activity (144) along the route;construction activity along the route, weather condition(s) (142) (e.g.,precipitation, wind, etc.) corresponding to the route, etc.

In embodiment(s), the configuration data can specify a configuration of,e.g., a temperature of an interior of the autonomous vehicle; a seatingconfiguration of the interior; a multimedia configuration of theinterior, e.g., representing a music stream, movie stream, sport/sportsteam audio/video stream, news service stream, etc. to be presented tothe subscriber via multimedia devices (see e.g., vehicle devices 325below) of the autonomous vehicle; a navigation system of the autonomousvehicle; an acceleration, braking, and steering system of the autonomousvehicle, e.g., to facilitate control of travel of the autonomousvehicle, e.g., along the route, along a detour of the route, etc.

In this regard, in an embodiment, based on the route informationrepresenting, e.g., high traffic occurring on the route, police and/oremergency responder activity occurring on the route, constructionactivity occurring on the route, etc., configuration component 220 cangenerate the configuration data to facilitate control of the navigationsystem, control of the acceleration, braking, and steering system, etc.of the autonomous vehicle, e.g., to facilitate transport of thesubscriber to the destination along the detour.

In another embodiment, based on the profile information representing aselection of a type of the autonomous vehicle (e.g., size, brand, model,etc.), configuration component 220 can select, via an inventory ofautonomous vehicles (not shown) that has been maintained by a providerof the autonomous vehicle service, a “best selection” of autonomousvehicle from the inventory to facilitate the transport of the subscriberby the best selection of the autonomous vehicle. In this regard, thebest selection can represent a selection, from the inventory, of anautonomous vehicle, e.g., satisfying a majority of parameters, e.g.,size, brand, model, availability, etc. of an autonomous vehicle thathave been represented by the profile information; satisfying a keyparameter, e.g., availability, size, etc. of the autonomous vehicle thathas been represented by the profile information, etc.

In yet another embodiment, encryption component 240 can encrypt theconfiguration data according to manufacturing specification(s) for aspecific brand, type, etc. of the autonomous vehicle to generateencrypted data, and send the encrypted data directed to the autonomousvehicle to facilitate the transport by the autonomous vehicle.

In another embodiment, service component 250 can configure theautonomous vehicle service based on tiered service and pricing. Forexample, service component 250 can facilitate a selection, by thesubscriber, of a class of autonomous vehicle service of a group ofclasses of autonomous vehicle service, e.g., comprising a luxury class,a tourist class, a sport class, etc. based on respective prices of theclasses of autonomous vehicle service. In this regard, service component250 can facilitate billing the subscriber for a class of autonomousvehicle service that the subscriber has selected according to a price ofthe respective prices corresponding to the class of autonomous vehicleservice.

Now referring to FIG. 3, a block diagram (300) of a vehicle system (135)comprising sensors (310), electronic control units (320), vehicledevices (325), a processing component (330) comprising middleware (335),passenger interface (350), radio module (340), and antenna(s) (345) isillustrated, in accordance with various embodiments. In this regard,middleware 335 comprises computer-readable media for storinginstructions that can be executed by processing component 330 forperforming various operations of the autonomous vehicle (e.g., 130,150).

In embodiment(s), the operations can comprise receiving, via a wirelessinterface (e.g., 114, 116) using radio module 340 and antenna(s) 345,the configuration data from dynamic recommendation engine 110, andcontrolling, based on the configuration data, vehicle devices 325, e.g.,climate control devices, seating devices, suspension devices, steeringdevices, acceleration devices, braking devices, navigation devices,warning devices, entertainment, e.g., multimedia, devices, etc. of theautonomous vehicle.

In embodiment(s), radio module 340 can comprise a long-term evolution(LTE) based radio module corresponding to new radio access technology,e.g., operable from sub-gigahertz (GHz), e.g., 1 GHz, to 100 GHz. Inother embodiment(s), middleware 335 can comprise OEM firmware forconfiguring, controlling, etc. electronic control units 320corresponding to respective devices of vehicle devices 325.

In other embodiment(s), passenger interface 350 can be used to receivean override input from the passenger enabling the passenger to overrideone or more autonomous features controlled by the configuration data. Inthis regard, based on the override input, middleware 335 can enable thepassenger to adjust, control, modify, etc. the configuration data tofacilitate control, by the passenger, of vehicle devices 325.

FIGS. 4-8 illustrate methodologies in accordance with the disclosedsubject matter. For simplicity of explanation, the methodologies aredepicted and described as a series of acts. It is to be understood andappreciated that various embodiments disclosed herein are not limited bythe acts illustrated and/or by the order of acts. For example, acts canoccur in various orders and/or concurrently, and with other acts notpresented or described herein. Furthermore, not all illustrated acts maybe required to implement the methodologies in accordance with thedisclosed subject matter. In addition, those skilled in the art willunderstand and appreciate that the methodologies could alternatively berepresented as a series of interrelated states via a state diagram orevents. Additionally, it should be further appreciated that themethodologies disclosed hereinafter and throughout this specificationare capable of being stored on an article of manufacture to facilitatetransporting and transferring such methodologies to computers. The termarticle of manufacture, as used herein, is intended to encompass acomputer program accessible from any computer-readable device, carrier,or media.

Referring now to FIG. 4, a process (400) performed by a system, e.g.,dynamic recommendation engine 110, is illustrated, in accordance withvarious example embodiments. At 410, the system can receive a requestfrom a subscriber of an autonomous vehicle service—the requestspecifying a route of transport by an autonomous vehicle. At 420, thesystem can obtain, via a network slice comprising a virtual networkfunction of the autonomous vehicle service, profile information for thesubscriber and route information for the route. At 430, the system candetermine, via the network slice based on the profile information andthe route information, configuration data for the autonomous vehicle. At440, the system can send, via the network slice, the configuration datadirected to the autonomous vehicle to facilitate the transport by theautonomous vehicle.

FIGS. 5 and 6 illustrate another process (500 and 600) performed by thesystem, e.g., dynamic recommendation engine 110, in accordance withvarious embodiments. At 510, it can be determined whether a requestassociated with a subscriber identity of an autonomous vehicle servicehas been received. In this regard, in response to a determination thatthe request has been received, flow continues to 520, at which thesystem can instantiate a network slice of the autonomous vehicle serviceas a virtual function to facilitate a transport, via an autonomousvehicle, of a subscriber identified by the subscriber identity accordingto a route that has been specified by the request. At 530, the systemcan obtain, using the network slice, a profile of the subscriberidentity and route information of the route. Flow continues from 530 to610, at which the system can determine, based on the profile and theroute information using the network slice, configuration data. At 610,the system can send, using the network slice, the configuration datadirected to the autonomous vehicle to facilitate the transport of thesubscriber.

FIGS. 7-8 illustrate a process (700 and 800) performed by the system,e.g., dynamic recommendation engine 110, to facilitate transport of thesubscriber to a destination along a detour of a route corresponding tothe destination, in accordance with various embodiments. In this regard,at 710, it can be determined whether route information representspolice, e.g., radar, and/or emergency responder activity occurring onthe route. In this regard, in response to a determination that the routeinformation represents police and/or emergency responder activityoccurring on the route, flow continues to 720, at which the system cangenerate, via the network slice, the configuration data to facilitatetransport of the subscriber to the destination along the detour of theroute; otherwise flow continues to 810.

At 810, it can be determined whether the route information representshigh traffic, e.g., with respect to representing a defined level ofdelay, e.g., more than 10 minutes, above an average commute timecorresponding to the route, and/or construction activity occurring onthe route. In this regard, in response to a determination that the routeinformation represents high traffic and/or construction activityoccurring on the route, flow continues to 820, at which the system cangenerate, via the network slice, the configuration data to facilitatetransport of the subscriber to the destination along the detour of theroute; otherwise flow returns to 710.

With respect to FIG. 9, a wireless communication environment 900including macro network platform 910 is illustrated, in accordance withvarious embodiments. Macro network platform 910 serves or facilitatescommunication with a vehicle system (e.g., 135) of an autonomous vehicle(e.g., 130, 150) and autonomous vehicle service slice 120 via wirelessnetwork 101. It should be appreciated that in cellular wirelesstechnologies, e.g., 3GPP UMTS, high speed packet access (HSPA), 3GPPLTE, third generation partnership project 2 (3GPP2), ultra-mobilebroadband (UMB), LTE-A, etc. that can be associated with wirelessnetwork 101, macro network platform 910 can be embodied in a corenetwork. It is noted that wireless network 101 can include basestation(s) (e.g., 104, 106), base transceiver station(s), accesspoint(s), etc. and associated electronic circuitry and deploymentsite(s), in addition to a wireless radio link (e.g., 114, 116) operatedin accordance with the base station(s), etc. Accordingly, wirelessnetwork 101 can comprise various coverage cells, or wireless coverageareas. In addition, it should be appreciated that elements and/orcomponents (e.g., dynamic recommendation engine 110, data store 122) ofautonomous vehicle service slice 120 can be located/included within oneor more components/elements, e.g., hardware, software, etc., of wirelesscommunication environment 900, e.g., macro network platform 910,wireless network 101, etc.

Generally, macro network platform 910 includes components, e.g., nodes,GWs, interfaces, servers, platforms, etc. that facilitate bothpacket-switched (PS), e.g., IP, frame relay, asynchronous transfer mode(ATM), and circuit-switched (CS) traffic, e.g., voice and data, andcontrol generation for networked wireless communication, e.g., viadynamic recommendation engine 110. In various embodiments, macro networkplatform 910 includes CS gateway (GW) node(s) 912 that can interface CStraffic received from legacy networks like telephony network(s) 940,e.g., public switched telephone network (PSTN), public land mobilenetwork (PLMN), Signaling System No. 7 (SS7) network 960, etc. CS GWnode(s) 912 can authorize and authenticate traffic, e.g., voice, arisingfrom such networks. Additionally, CS GW node(s) 912 can access mobilityor roaming data generated through SS7 network 960; for instance,mobility data stored in a visitor location register (VLR), which canreside in memory 930. Moreover, CS GW node(s) 912 interfaces CS-basedtraffic and signaling with PS GW node(s) 918. As an example, in a 3GPPUMTS network, PS GW node(s) 918 can be embodied in GW general packetradio service (GPRS) support node(s) (GGSN).

As illustrated by FIG. 9, PS GW node(s) 918 can receive and processCS-switched traffic and signaling via CS GW node(s) 912. Further PS GWnode(s) 918 can authorize and authenticate PS-based data sessions, e.g.,via wireless network 101, with served devices, communication devices,etc. Such data sessions can include traffic exchange with networksexternal to macro network platform 910, like wide area network(s) (WANs)950; enterprise networks (NWs) 970, e.g., E911, service NW(s) 980, e.g.,an IP multimedia subsystem (IMS), etc. It should be appreciated thatlocal area network(s) (LANs), which may be a part of enterprise NW(s)970, can also be interfaced with macro network platform 910 through PSGW node(s) 918. PS GW node(s) 918 can generate packet data contexts whena data session is established, e.g., associated with an EPS bearercontext activation. To that end, in an aspect, PS GW node(s) 918 caninclude a tunnel interface, e.g., tunnel termination GW (TTG) in 3GPPUMTS network(s) (not shown), which can facilitate packetizedcommunication with disparate wireless network(s), such as Wi-Finetworks. It should be further appreciated that the packetizedcommunication can include multiple flows that can be generated throughserver(s) 914. It is to be noted that in 3GPP UMTS network(s), PS GWnode(s) 918 (e.g., GGSN) and tunnel interface (e.g., TTG) comprise apacket data GW (PDG).

Macro network platform 910 also includes serving node(s) 916 that canconvey the various packetized flows of information, or data streams,received through PS GW node(s) 918. As an example, in a 3GPP UMTSnetwork, serving node(s) can be embodied in serving GPRS support node(s)(SGSN).

As indicated above, server(s) 914 in macro network platform 910 canexecute numerous applications, e.g., messaging, location services,wireless device management, etc. that can generate multiple disparatepacketized data streams or flows; and can manage such flows, e.g.,schedule, queue, format. Such application(s), for example can includeadd-on features to standard services provided by macro network platform910. Data streams can be conveyed to PS GW node(s) 918 forauthorization/authentication and initiation of a data session, and toserving node(s) 916 for communication thereafter. Server(s) 914 can alsoeffect security, e.g., implement one or more firewalls, of macro networkplatform 910 to ensure network's operation and data integrity inaddition to authorization and authentication procedures that CS GWnode(s) 912 and PS GW node(s) 918 can enact. Moreover, server(s) 914 canprovision services from external network(s), e.g., WAN 950, or globalpositioning system (GPS) network(s), which can be a part of enterpriseNW(s) 980. It is to be noted that server(s) 914 can include one or moreprocessors configured to confer at least in part the functionality ofmacro network platform 910. To that end, the one or more processors canexecute code instructions stored in memory 930, for example.

In wireless communication environment 900, memory 930 can storeinformation related to operation of macro network platform 910, e.g.,related to operation of an autonomous vehicle (e.g., 130, 150), dynamicrecommendation engine 110, etc. The information can include data,business data, etc. associated with subscribers of respective services;market plans and strategies, e.g., promotional campaigns, businesspartnerships, mobile devices served through macro network platform,etc.; service and privacy information, policies, etc.; end-user servicelogs for law enforcement; term(s) and/or condition(s) associated withwireless service(s) provided via wireless network 101; and so forth.Memory 930 can also store information from at least one of telephonynetwork(s) 940, WAN 950, SS7 network 960, enterprise NW(s) 970, orservice NW(s) 980.

In one or more embodiments, components of core network environment 900can provide, e.g., via autonomous vehicle service slice 120,communication services to the autonomous vehicle utilizing anover-the-air wireless link (e.g., 114, 116) via wireless network 101. Inthis regard, wireless network 101 can include one or more: macro, Femto,or pico access points (APs) (not shown); base stations (BS) (not shown);landline networks (e.g., optical landline networks, electrical landlinenetworks) (not shown) communicatively coupled between the autonomousvehicle and macro network platform 910, etc.

Core network environment 900 can include one or more of the Internet (oranother communication network (e.g., IP-based network)), or DSL-type orbroadband network facilitated by Ethernet or other technology. Invarious embodiments, core network environment 900 can include hardwareand/or software for allocating resources to the autonomous vehicle anddynamic recommendation engine 110, converting or enforcing protocols,establishing and/or providing levels of quality of service (QoS),providing applications or services, translating signals, and/orperforming other desired functions to facilitate system interoperabilityand communication to/from the autonomous vehicle and dynamicrecommendation engine 110.

In other embodiment(s), core network environment 900 can include datastore component(s), a memory configured to store information,computer-readable storage media storing computer-executableinstructions, e.g., memory 930, etc. enabling various operationsperformed via dynamic recommendation engine as described herein.

As it employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to comprising, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit (ASIC), a digitalsignal processor (DSP), a field programmable gate array (FPGA), aprogrammable logic controller (PLC), a complex programmable logic device(CPLD), a discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsand/or processes described herein. Processors can exploit nano-scalearchitectures such as, but not limited to, molecular and quantum-dotbased transistors, switches and gates, in order to optimize space usageor enhance performance of mobile devices. A processor may also beimplemented as a combination of computing processing units.

In the subject specification, terms such as “store,” “data store,” “datastorage,” “middleware,” “memory storage,” and substantially any otherinformation storage component relevant to operation and functionality ofa component and/or process, refer to “memory components,” or entitiesembodied in a “memory,” or components comprising the memory. It will beappreciated that the memory components described herein can be eithervolatile memory or nonvolatile memory, or can include both volatile andnonvolatile memory.

By way of illustration, and not limitation, nonvolatile memory, forexample, can be included in data store 122, middleware 335, memory 930,non-volatile memory 1022 (see below), disk storage 1024 (see below),and/or memory storage 1046 (see below). Further, nonvolatile memory canbe included in read only memory (ROM), programmable ROM (PROM),electrically programmable ROM (EPROM), electrically erasable ROM(EEPROM), or flash memory. Volatile memory 1020 can include randomaccess memory (RAM), which acts as external cache memory. By way ofillustration and not limitation, RAM is available in many forms such assynchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM),double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), SynchlinkDRAM (SLDRAM), and direct Rambus RAM (DRRAM). Additionally, thedisclosed memory components of systems or methods herein are intended tocomprise, without being limited to comprising, these and any othersuitable types of memory.

In order to provide a context for the various aspects of the disclosedsubject matter, FIG. 10, and the following discussion, are intended toprovide a brief, general description of a suitable environment in whichthe various aspects of the disclosed subject matter can be implemented.While the subject matter has been described above in the general contextof computer-executable instructions of a computer program that runs on acomputer and/or computers, those skilled in the art will recognize thatvarious embodiments disclosed herein can be implemented in combinationwith other program modules. Generally, program modules include routines,programs, components, data structures, etc. that perform particulartasks and/or implement particular abstract data types.

Moreover, those skilled in the art will appreciate that the inventivesystems can be practiced with other computer system configurations,including single-processor or multiprocessor computer systems, computingdevices, mini-computing devices, mainframe computers, as well aspersonal computers, hand-held computing devices (e.g., PDA, phone,watch), microprocessor-based or programmable consumer or industrialelectronics, and the like. The illustrated aspects can also be practicedin distributed computing environments where tasks are performed byremote processing devices that are linked through a communicationnetwork; however, some if not all aspects of the subject disclosure canbe practiced on stand-alone computers. In a distributed computingenvironment, program modules can be located in both local and remotememory storage devices.

With reference to FIG. 10, a block diagram of a computing system 1000operable to execute the disclosed systems and methods is illustrated, inaccordance with an embodiment. Computer 1012 includes a processing unit1014, a system memory 1016, and a system bus 1018. System bus 1018couples system components including, but not limited to, system memory1016 to processing unit 1014. Processing unit 1014 can be any of variousavailable processors. Dual microprocessors and other multiprocessorarchitectures also can be employed as processing unit 1014.

System bus 1018 can be any of several types of bus structure(s)including a memory bus or a memory controller, a peripheral bus or anexternal bus, and/or a local bus using any variety of available busarchitectures including, but not limited to, industrial standardarchitecture (ISA), micro-channel architecture (MSA), extended ISA(EISA), intelligent drive electronics (IDE), VESA local bus (VLB),peripheral component interconnect (PCI), card bus, universal serial bus(USB), advanced graphics port (AGP), personal computer memory cardinternational association bus (PCMCIA), Firewire (IEEE 1394), smallcomputer systems interface (SCSI), and/or controller area network (CAN)bus used in vehicles.

System memory 1016 includes volatile memory 1020 and nonvolatile memory1022. A basic input/output system (BIOS), containing routines totransfer information between elements within computer 1012, such asduring start-up, can be stored in nonvolatile memory 1022. By way ofillustration, and not limitation, nonvolatile memory 1022 can includeROM, PROM, EPROM, EEPROM, or flash memory. Volatile memory 1020 includesRAM, which acts as external cache memory. By way of illustration and notlimitation, RAM is available in many forms such as SRAM, dynamic RAM(DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM),enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), Rambus direct RAM(RDRAM), direct Rambus dynamic RAM (DRDRAM), and Rambus dynamic RAM(RDRAM).

Computer 1012 also includes removable/non-removable,volatile/non-volatile computer storage media. FIG. 10 illustrates, forexample, disk storage 1024. Disk storage 1024 includes, but is notlimited to, devices like a magnetic disk drive, floppy disk drive, tapedrive, Jaz drive, Zip drive, LS-100 drive, flash memory card, or memorystick. In addition, disk storage 1024 can include storage mediaseparately or in combination with other storage media including, but notlimited to, an optical disk drive such as a compact disk ROM device(CD-ROM), CD recordable drive (CD-R Drive), CD rewritable drive (CD-RWDrive) or a digital versatile disk ROM drive (DVD-ROM). To facilitateconnection of the disk storage devices 1024 to system bus 1018, aremovable or non-removable interface is typically used, such asinterface 1026.

It is to be appreciated that FIG. 10 describes software that acts as anintermediary between users and computer resources described in suitableoperating environment 1000. Such software includes an operating system1028. Operating system 1028, which can be stored on disk storage 1024,acts to control and allocate resources of computer system 1012. Systemapplications 1030 take advantage of the management of resources byoperating system 1028 through program modules 1032 and program data 1034stored either in system memory 1016 or on disk storage 1024. It is to beappreciated that the disclosed subject matter can be implemented withvarious operating systems or combinations of operating systems.

A user can enter commands or information into computer 1012 throughinput device(s) 1036. Input devices 1036 include, but are not limitedto, a pointing device such as a mouse, trackball, stylus, touch pad,keyboard, microphone, joystick, game pad, satellite dish, scanner, TVtuner card, digital camera, digital video camera, web camera, cellularphone, user equipment, smartphone, and the like. These and other inputdevices connect to processing unit 1014 through system bus 1018 viainterface port(s) 1038. Interface port(s) 1038 include, for example, aserial port, a parallel port, a game port, a universal serial bus (USB),a wireless based port, e.g., WiFi, Bluetooth, etc. Output device(s) 1040use some of the same type of ports as input device(s) 1036.

Thus, for example, a USB port can be used to provide input to computer1012 and to output information from computer 1012 to an output device1040. Output adapter 1042 is provided to illustrate that there are someoutput devices 1040, like display devices, light projection devices,monitors, speakers, and printers, among other output devices 1040, whichuse special adapters. Output adapters 1042 include, by way ofillustration and not limitation, video and sound devices, cards, etc.that provide means of connection between output device 1040 and systembus 1018. It should be noted that other devices and/or systems ofdevices provide both input and output capabilities such as remotecomputer(s) 1044.

Computer 1012 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)1044. Remote computer(s) 1044 can be a personal computer, a server, arouter, a network PC, a workstation, a microprocessor based appliance, apeer device, or other common network node and the like, and typicallyincludes many or all of the elements described relative to computer1012.

For purposes of brevity, only a memory storage device (e.g., 1046) isillustrated with remote computer(s) 1044. Remote computer(s) 1044 islogically connected to computer 1012 through a network interface 1048and then physically and/or wirelessly connected via communicationconnection 1050. Network interface 1048 encompasses wire and/or wirelesscommunication networks such as local-area networks (LAN) and wide-areanetworks (WAN). LAN technologies include fiber distributed datainterface (FDDI), copper distributed data interface (CDDI), Ethernet,token ring and the like. WAN technologies include, but are not limitedto, point-to-point links, circuit-switching networks like integratedservices digital networks (e.g., ISDN) and variations thereon, packetswitching networks, and digital subscriber lines (DSL).

Communication connection(s) 1050 refer(s) to hardware/software employedto connect network interface 1048 to bus 1018. While communicationconnection 1050 is shown for illustrative clarity inside computer 1012,it can also be external to computer 1012. The hardware/software forconnection to network interface 1048 can include, for example, internaland external technologies such as modems, including regular telephonegrade modems, cable modems and DSL modems, wireless modems, ISDNadapters, and Ethernet cards.

The computer 1012 can operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, cellular based devices, user equipment, smartphones,or other computing devices, such as workstations, server computers,routers, personal computers, portable computers, microprocessor-basedentertainment appliances, peer devices or other common network nodes,etc. The computer 1012 can connect to other devices/networks by way ofantenna, port, network interface adaptor, wireless access point, modem,and/or the like.

The computer 1012 is operable to communicate with any wireless devicesor entities operatively disposed in wireless communication, e.g., aprinter, scanner, desktop and/or portable computer, portable dataassistant, communications satellite, user equipment, cellular basedevice, smartphone, any piece of equipment or location associated with awirelessly detectable tag (e.g., scanner, a kiosk, news stand,restroom), and telephone. This includes at least WiFi and Bluetoothwireless technologies. Thus, the communication can be a predefinedstructure as with a conventional network or simply an ad hoccommunication between at least two devices.

WiFi allows connection to the Internet from a desired location (e.g., avehicle, couch at home, a bed in a hotel room, or a conference room atwork, etc.) without wires. WiFi is a wireless technology similar to thatused in a cell phone that enables such devices, e.g., mobile phones,computers, etc., to send and receive data indoors and out, anywherewithin the range of a base station. WiFi networks use radio technologiescalled IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, fastwireless connectivity. A WiFi network can be used to connect devices(e.g., mobile phones, computers, etc.) to each other, to the Internet,and to wired networks (which use IEEE 802.3 or Ethernet). WiFi networksoperate in the unlicensed 2.4 and 5 GHz radio bands, at an 11 Mbps(802.11a) or 54 Mbps (802.11b) data rate, for example, or with productsthat contain both bands (dual band), so the networks can providereal-world performance similar to the basic 10BaseT wired Ethernetnetworks used in many offices.

As utilized herein, terms “component,” “system,” “server,” “interface,”and the like are intended to refer to a computer-related entity,hardware, software (e.g., in execution), and/or firmware. For example, acomponent can be a processor, a process running on a processor, anobject, an executable, a program, a storage device, and/or a computer.By way of illustration, an application running on a server and theserver can be a component. One or more components can reside within aprocess, and a component can be localized on one computer and/ordistributed between two or more computers.

Aspects of systems, apparatus, and processes explained herein canconstitute machine-executable instructions embodied within a machine,e.g., embodied in a computer readable medium (or media) associated withthe machine. Such instructions, when executed by the machine, can causethe machine to perform the operations described. Additionally, systems,processes, process blocks, etc. can be embodied within hardware, such asan application specific integrated circuit (ASIC) or the like. Moreover,the order in which some or all of the process blocks appear in eachprocess should not be deemed limiting. Rather, it should be understoodby a person of ordinary skill in the art having the benefit of theinstant disclosure that some of the process blocks can be executed in avariety of orders not illustrated.

Further, components can execute from various computer readable mediahaving various data structures stored thereon. The components cancommunicate via local and/or remote processes such as in accordance witha signal having one or more data packets (e.g., data from one componentinteracting with another component in a local system, distributedsystem, and/or across a network, e.g., the Internet, with other systemsvia the signal).

As another example, a component can be an apparatus with specificfunctionality provided by mechanical parts operated by electric orelectronic circuitry; the electric or electronic circuitry can beoperated by a software application or a firmware application executed byone or more processors; the one or more processors can be internal orexternal to the apparatus and can execute at least a part of thesoftware or firmware application. As yet another example, a componentcan be an apparatus that provides specific functionality throughelectronic components without mechanical parts; the electroniccomponents can include one or more processors therein to executesoftware and/or firmware that confer(s), at least in part, thefunctionality of the electronic components.

Further, aspects, features, and/or advantages of the disclosed subjectmatter can be exploited in substantially any wireless telecommunicationor radio technology, e.g., IEEE 802.XX technology, e.g., Wi-Fi,Bluetooth, etc.; WiMAX; enhanced GPRS; 3GPP LTE; 3GPP2; UMB; 3GPP UMTS;HSPA; high speed downlink packet access (HSDPA); high speed uplinkpacket access (HSUPA); LTE-A, GSM, NFC, Wibree, Zigbee, satellite, Wi-FiDirect, etc.

Further, selections of a radio technology, or radio access technology,can include second generation (2G), third generation (3G), fourthgeneration (4G), fifth generation (5G), x^(th) generation, etc.evolution of the radio access technology; however, such selections arenot intended as a limitation of the disclosed subject matter and relatedaspects thereof. Further, aspects, features, and/or advantages of thedisclosed subject matter can be exploited in disparate electromagneticfrequency bands. Moreover, one or more embodiments described herein canbe executed in one or more network elements, such as a mobile wirelessdevice, e.g., UE, and/or within one or more elements of a networkinfrastructure, e.g., radio network controller, wireless access point(AP), etc.

Moreover, terms like “user equipment,” (UE) “mobile station,” “mobilesubscriber station,” “access terminal,” “terminal”, “handset,”“appliance,” “machine,” “wireless communication device,” “cellularphone,” “personal digital assistant,” “smartphone,” “wireless device”,and similar terminology refer to a wireless device, or wirelesscommunication device, which is at least one of (1) utilized by asubscriber of a wireless service, or communication service, to receiveand/or convey data associated with voice, video, sound, and/orsubstantially any data-stream or signaling-stream; or (2) utilized by asubscriber of a voice over IP (VoIP) service that delivers voicecommunications over IP networks such as the Internet or otherpacket-switched networks. Further, the foregoing terms are utilizedinterchangeably in the subject specification and related drawings.

A communication network, e.g., corresponding to an autonomous vehicleecosystem comprising an autonomous vehicle service slice and dynamicrecommendation engine, (see e.g., 100), for systems, methods, and/orapparatus disclosed herein can include any suitable mobile and/orwireline-based circuit-switched communication network including a GSMnetwork, a time division multiple access (TDMA) network, a code divisionmultiple access (CDMA) network, such as an Interim Standard 95 (IS-95)and subsequent iterations of CDMA technology, an integrated digitalenhanced network (iDEN) network and a PSTN. Further, examples of thecommunication network can include any suitable data packet-switched orcombination data packet/circuit-switched communication network, wired orwireless IP network such as a VoLTE network, a VoIP network, an IP datanetwork, a UMTS network, a GPRS network, or other communication networksthat provide streaming data communication over IP and/or integratedvoice and data communication over combination datapacket/circuit-switched technologies.

Similarly, one of ordinary skill in the art will appreciate that awireless system e.g., a wireless communication device, vehicle system135, etc. for systems, methods, and/or apparatus disclosed herein caninclude a mobile device, a mobile phone, a 4G, a 5G, etc. cellularcommunication device, a PSTN phone, a cellular communication device, acellular phone, a satellite communication device, a satellite phone, aVoIP phone, WiFi phone, a dual-mode cellular/WiFi phone, a combinationcellular/VoIP/WiFi/WiMAX phone, a portable computer, or any suitablecombination thereof. Specific examples of a wireless system can include,but are not limited to, a cellular device, such as a GSM, TDMA, CDMA,IS-95 and/or iDEN phone, a cellular/WiFi device, such as a dual-modeGSM, TDMA, IS-95 and/or iDEN/VoIP phones, UMTS phones, UMTS VoIP phones,or like devices or combinations thereof.

The disclosed subject matter can be implemented as a method, apparatus,or article of manufacture using standard programming and/or engineeringtechniques to produce software, firmware, hardware, or any combinationthereof to control a computer to implement the disclosed subject matter.The term “article of manufacture” as used herein is intended toencompass a computer program accessible from any computer-readabledevice, computer-readable carrier, or computer-readable media. Forexample, computer-readable media can include, but are not limited to,magnetic storage devices, e.g., hard disk; floppy disk; magneticstrip(s); optical disk (e.g., compact disk (CD), digital video disc(DVD), Blu-ray Disc (BD)); smart card(s); and flash memory device(s)(e.g., card, stick, key drive); and/or a virtual device that emulates astorage device and/or any of the above computer-readable media.

In accordance with various aspects of the subject specification,artificial intelligence based systems, components, etc. can employclassifier(s) that are explicitly trained, e.g., via a generic trainingdata, via policy rules of a policy framework, etc. as well as implicitlytrained, e.g., via observing characteristics of communication equipment,e.g., a gateway, a wireless communication device, etc., by receivingreports from such communication equipment, by receiving operatorpreferences, by receiving historical information, by receiving extrinsicinformation, etc.

For example, support vector machines can be configured via a learning ortraining phase within a classifier constructor and feature selectionmodule, component, etc. Thus, the classifier(s) can be used by anartificial intelligence system to automatically learn and perform anumber of functions, e.g., performed by a system (e.g., dynamicrecommendation engine 110), including but not limited to: in response toreceiving, from a subscriber of an autonomous vehicle service, a requestspecifying a route of transport by an autonomous vehicle, obtaining, viaa network slice comprising a virtual network function of the autonomousvehicle service, profile information for the subscriber and routeinformation for the route; and in response to determining, via thenetwork slice based on the profile information and the routeinformation, configuration data for the autonomous vehicle, sending, viathe network slice, the configuration data directed to the autonomousvehicle to facilitate the transport by the autonomous vehicle.

A classifier can be a function that maps an input attribute vector,x=(x1, x2, x3, x4, xn), to a confidence that the input belongs to aclass, that is, f(x)=confidence (class). Such classification can employa probabilistic and/or statistical-based analysis (e.g., factoring intothe analysis utilities and costs) to infer an action that a user, e.g.,subscriber, desires to be automatically performed. In the case ofcommunication systems, for example, attributes can be informationreceived from access points, services, components of a wirelesscommunication network, etc., and the classes can be categories or areasof interest (e.g., levels of priorities). A support vector machine is anexample of a classifier that can be employed. The support vector machineoperates by finding a hypersurface in the space of possible inputs,which the hypersurface attempts to split the triggering criteria fromthe non-triggering events. Intuitively, this makes the classificationcorrect for testing data that is near, but not identical to trainingdata. Other directed and undirected model classification approachesinclude, e.g., naïve Bayes, Bayesian networks, decision trees, neuralnetworks, fuzzy logic models, and probabilistic classification modelsproviding different patterns of independence can be employed.Classification as used herein can also be inclusive of statisticalregression that is utilized to develop models of priority.

As used herein, the term “infer” or “inference” refers generally to theprocess of reasoning about, or inferring states of, the system,environment, user, and/or intent from a set of observations as capturedvia events and/or data. Captured data and events can include user data,device data, environment data, data from sensors, sensor data,application data, implicit data, explicit data, etc. Inference can beemployed to identify a specific context or action, or can generate aprobability distribution over states of interest based on aconsideration of data and events, for example.

Inference can also refer to techniques employed for composinghigher-level events from a set of events and/or data. Such inferenceresults in the construction of new events or actions from a set ofobserved events and/or stored event data, whether the events arecorrelated in close temporal proximity, and whether the events and datacome from one or several event and data sources. Various classificationschemes and/or systems (e.g., support vector machines, neural networks,expert systems, Bayesian belief networks, fuzzy logic, and data fusionengines) can be employed in connection with performing automatic and/orinferred action in connection with the disclosed subject matter.

Further, the word “exemplary” and/or “demonstrative” is used herein tomean serving as an example, instance, or illustration. For the avoidanceof doubt, the subject matter disclosed herein is not limited by suchexamples. In addition, any aspect or design described herein as“exemplary” and/or “demonstrative” is not necessarily to be construed aspreferred or advantageous over other aspects or designs, nor is it meantto preclude equivalent exemplary structures and techniques known tothose of ordinary skill in the art having the benefit of the instantdisclosure.

Furthermore, to the extent that the terms “includes,” “has,” “contains,”and other similar words are used in either the detailed description orthe appended claims, such terms are intended to be inclusive—in a mannersimilar to the term “comprising” as an open transition word—withoutprecluding any additional or other elements. Moreover, the term “or” isintended to mean an inclusive “or” rather than an exclusive “or”. Thatis, unless specified otherwise, or clear from context, “X employs A orB” is intended to mean any of the natural inclusive permutations. Thatis, if X employs A; X employs B; or X employs both A and B, then “Xemploys A or B” is satisfied under any of the foregoing instances. Inaddition, the articles “a” and “an” as used in this application and theappended claims should generally be construed to mean “one or more”unless specified otherwise or clear from context to be directed to asingular form.

The above description of illustrated embodiments of the subjectdisclosure, including what is described in the Abstract, is not intendedto be exhaustive or to limit the disclosed embodiments to the preciseforms disclosed. While specific embodiments and examples are describedherein for illustrative purposes, various modifications are possiblethat are considered within the scope of such embodiments and examples,as those skilled in the relevant art can recognize.

In this regard, while the disclosed subject matter has been described inconnection with various embodiments and corresponding Figures, whereapplicable, it is to be understood that other similar embodiments can beused or modifications and additions can be made to the describedembodiments for performing the same, similar, alternative, or substitutefunction of the disclosed subject matter without deviating therefrom.Therefore, the disclosed subject matter should not be limited to anysingle embodiment described herein, but rather should be construed inbreadth and scope in accordance with the appended claims below.

What is claimed is:
 1. A system, comprising: a processor; and a memorythat stores executable instructions that, when executed by theprocessor, facilitate performance of operations, comprising: in responseto receiving, from a subscriber of an autonomous vehicle service, arequest specifying a route of transport by an autonomous vehicle,obtaining, via a network slice comprising a virtual network function ofthe autonomous vehicle service, profile information for the subscriberand route information for the route; and in response to determining, viathe network slice based on the profile information and the routeinformation, configuration data for the autonomous vehicle, sending, viathe network slice, the configuration data directed to the autonomousvehicle to facilitate the transport by the autonomous vehicle.
 2. Thesystem of claim 1, wherein the profile information represents aselection of a type of the autonomous vehicle.
 3. The system of claim 1,wherein the profile information represents a selection of a class of theautonomous vehicle service.
 4. The system of claim 1, wherein profileinformation represents a selection of an interior configuration of theautonomous vehicle with respect to at least one of a temperature of theinterior, a seating configuration of the interior, or an entertainmentconfiguration of the interior.
 5. The system of claim 1, wherein theroute comprises a first route, and wherein the profile informationrepresents a preference of the subscriber corresponding to a secondroute.
 6. The system of claim 1, wherein the subscriber is a firstoccupant, wherein the autonomous vehicle is a first vehicle, and whereinthe operations further comprise: deriving the profile information basedon occupant information representing a preference of a second occupantof a second vehicle associated with the route.
 7. The system of claim 1,wherein the route information comprises traffic information for theroute.
 8. The system of claim 1, wherein the configuration dataspecifies a configuration for at least one of a temperature of aninterior of the autonomous vehicle, a seating configuration of theinterior, a multimedia configuration of the interior, or a direction ofthe autonomous vehicle.
 9. The system of claim 1, wherein the virtualnetwork function corresponds to a group of software network functions ofrespective vehicle services comprising the autonomous vehicle service.10. The system of claim 9, wherein the network slice is a first networkslice, wherein the virtual network function is a first virtual networkfunction of the group of software network functions, wherein theautonomous vehicle service is a first service of the respective vehicleservices, and wherein the first network slice corresponds to firstcontrol and user planes that are distinct from second control and userplanes corresponding to a second network slice that is associated with asecond virtual network function of the group of software networkfunctions that is associated with a second service of the respectivevehicle services.
 11. The system of claim 1, wherein the operationsfurther comprise: in response to the receiving the request,instantiating the network slice in the memory to provision theautonomous vehicle service.
 12. A method, comprising: in response toreceiving a request associated with a subscriber identity of anautonomous vehicle service, instantiating, by a system comprising aprocessor, a network slice of the autonomous vehicle service as avirtual network function to facilitate a transport, via an autonomousvehicle, of a subscriber identified by the subscriber identity accordingto a route that has been specified by the request; and in response toobtaining, by the system using the network slice, a profile of thesubscriber identity and route information of the route, determining,based on the profile and the route information using the network slice,configuration data, and sending, using the network slice, theconfiguration data directed to the autonomous vehicle to facilitate thetransport of the subscriber.
 13. The method of claim 12, wherein theautonomous vehicle service is a first service, wherein the network sliceis a first slice, wherein the virtual network function is a firstvirtual network function, and wherein the instantiating comprises:instantiating the first slice using a first control plane, wherein thesecond slice has been instantiated as a second virtual network functionusing a second control plane that is different from the first controlplane.
 14. The method of claim 12, wherein the obtaining the profile ofthe subscriber identity comprises obtaining information presenting atleast one of an environmental preference for an interior of the vehicle,a seating configuration for the interior, or an entertainmentconfiguration for the interior.
 15. The method of claim 12, wherein thesubscriber is a first occupant, wherein the autonomous vehicle is afirst vehicle, and wherein the obtaining the profile of the firstoccupant comprises determining a preference of a second occupant of asecond vehicle corresponding to the route.
 16. The method of claim 12,wherein the determining the route information comprises: determining atraffic condition of the route.
 17. The method of claim 12, wherein thedetermining the configuration data comprises: determining a detour forthe route.
 18. A machine-readable storage medium, comprising executableinstructions that, when executed by a processor, facilitate performanceof operations, comprising: receiving, from a subscriber of a vehicleservice, a request for transportation, via an autonomous vehicle, to adestination; based on the request, obtaining, via a network slicecomprising a virtual network function, a subscriber profile of thesubscriber and route information of a route corresponding to thedestination; and based on the subscriber profile and the routeinformation, generating, via the network slice, configuration data forthe autonomous vehicle, and sending, via the network slice, theconfiguration data directed to the autonomous vehicle to facilitate thetransportation to the destination.
 19. The machine-readable storagemedium of claim 18, wherein the virtual network function is a firstvirtual function, and wherein the first virtual function corresponds toa first control plane that is different than a second control planecorresponding to a second virtual function.
 20. The machine-readablestorage medium of claim 18, wherein the generating the configurationdata comprises generating detour information to facilitate thetransportation to the destination via a detour.